# Gender Bias in English-to-Greek Machine Translation

arXiv: https://arxiv.org/abs/2506.09558 · HTML: https://mlsanity.com/paper/2506.09558/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 42 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 30 of 30)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Evaluating Gender Bias in Machine Translation](https://mlsanity.com/paper/1906.00591/) (2019)
- [Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology](https://mlsanity.com/paper/1906.04571/) (2019)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Getting Gender Right in Neural Machine Translation](https://mlsanity.com/paper/1909.05088/) (2019)
- [Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://mlsanity.com/paper/2004.04498/) (2020)
- [Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques](https://mlsanity.com/paper/1901.03116/) (2019)
- [Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation](https://mlsanity.com/paper/2109.03858/) (2021)
- [On Measuring Gender Bias in Translation of Gender-neutral Pronouns](https://mlsanity.com/paper/1905.11684/) (2019)
- [NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives](https://mlsanity.com/paper/2109.06105/) (2021)
- [Mitigating Gender Bias in Machine Translation with Target Gender Annotations](https://mlsanity.com/paper/2010.06203/) (2020)
- [Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It](https://mlsanity.com/paper/2010.05332/) (2020)
- [How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs](https://mlsanity.com/paper/1612.04629/) (2016)
- [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://mlsanity.com/paper/2211.01355/) (2022)
- [Gender Bias in Large Language Models across Multiple Languages](https://mlsanity.com/paper/2403.00277/) (2024)
- [Automatically Identifying Gender Issues in Machine Translation using Perturbations](https://mlsanity.com/paper/2004.14065/) (2020)
- [Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges](https://mlsanity.com/paper/2301.10075/) (2023)
- [Gender Coreference and Bias Evaluation at WMT 2020](https://mlsanity.com/paper/2010.06018/) (2020)
- [Gender Aware Spoken Language Translation Applied to English-Arabic](https://mlsanity.com/paper/1802.09287/) (2018)
- [Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives](https://mlsanity.com/paper/2107.11584/) (2021)
- [A Prompt Response to the Demand for Automatic Gender-Neutral Translation](https://mlsanity.com/paper/2402.06041/) (2024)
- [Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models](https://mlsanity.com/paper/2405.08477/) (2024)
- [GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages](https://mlsanity.com/paper/2402.14277/) (2024)
- [GOSt-MT: A Knowledge Graph for Occupation-related Gender Biases in Machine Translation](https://mlsanity.com/paper/2409.10989/) (2024)

## Built on by (most cited first, 3 of 3)

- [GAMBIT+: A Challenge Set for Evaluating Gender Bias in Machine Translation Quality Estimation Metrics](https://mlsanity.com/paper/2510.06841/) (2025)
- [What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Models](https://mlsanity.com/paper/2512.08440/) (2025)
- [Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution](https://mlsanity.com/paper/2607.22546/) (2026)

## Abstract (arXiv)

As the demand for inclusive language increases, concern has grown over the susceptibility of machine translation (MT) systems to reinforce gender stereotypes. This study investigates gender bias in two commercial MT systems, Google Translate and DeepL, focusing on the understudied English-to-Greek language pair. We address three aspects of gender bias: i) male bias, ii) occupational stereotyping, and iii) errors in anti-stereotypical translations. Additionally, we explore the potential of prompted GPT-4o as a bias mitigation tool that provides both gender-explicit and gender-neutral alternatives when necessary. To achieve this, we introduce GendEL, a manually crafted bilingual dataset of 240 gender-ambiguous and unambiguous sentences that feature stereotypical occupational nouns and adjectives. We find persistent gender bias in translations by both MT systems; while they perform well in cases where gender is explicitly defined, with DeepL outperforming both Google Translate and GPT-4o in feminine gender-unambiguous sentences, they are far from producing gender-inclusive or neutral translations when the gender is unspecified. GPT-4o shows promise, generating appropriate gendered and neutral alternatives for most ambiguous cases, though residual biases remain evident.